ArticleHeredity2022
Performance of Bayesian and BLUP alphabets for genomic prediction: analysis, comparison and results.
Article in Heredity, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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35 citing papers in PubMed.
- PGS-GS: a framework integrating polygenic scores and genomic selection in animal breeding.Briefings in bioinformatics · 2026Article
- Highly Polygenic Control of Photosynthetic Responses to Nighttime Temperature Studied by Genomic Prediction.Plant, cell & environment · 2026Article
- Evolutionary structure constrains genomic prediction accuracy more than model complexity in mango (Mangifera indica L.).G3 (Bethesda, Md.) · 2026Article
- Ensemble-based genomic prediction for maize flowering time improves prediction accuracy and reveals novel insights into trait genetic variation.G3 (Bethesda, Md.) · 2026Article
- Accelerating genetic gain through integrated genomic selection in crop plants.Journal of applied genetics · 2026Review
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- A Flexible Method for Genomics-Based Quantitative Genetics in Wild Study Systems-A Case Study on a House Sparrow Meta-Population.Evolutionary applications · 2026Article
- Integrating genomic prediction into crop DUS testing: new approaches in support of reference collection management and distinctness assessment.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- MeNet: A mixed-effect deep neural network for multi-environment genomic prediction of agronomic traits.Plant communications · 2026Article
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- High-Resolution Genomic Resources for Trait Mapping and Precision Breeding for Adzuki Bean (Vigna angularis).Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Adaptation of faba bean cultivars across Norwegian environments: identification of elite material and definition of optimal breeding and cultivar testing procedures.Frontiers in plant science · 2026Article
- Ensemble AnalySis with Interpretable Genomic Prediction (EasiGP): Computational tool for interpreting ensembles of genomic prediction models.The plant genome · 2025Article
- Comparative evaluation of SNP-weighted, Bayesian, and machine learning models for genomic prediction in Holstein cattle.BMC genomics · 2025Article
- Genetic parameters estimation and optimization of genomic selection in mud crab (Scylla paramamosain): a case study for growth-related traits.BMC genomics · 2025Article
- PotatoMASH is a cost-effective marker system for genomic prediction in potato based on short-read haplotypes.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Decoding hybrid origins and genetic architecture of leaf traits variation inHorticulture research · 2025Article
- Disentangling soybean GxE effects in an integrated genomic prediction and machine learning-GWAS workflow.Plant methods · 2025Article
- Improved genomic prediction performance with ensembles of diverse models.G3 (Bethesda, Md.) · 2025Article
- Advances in multi-trait genomic prediction approaches: classification, comparative analysis, and perspectives.Briefings in bioinformatics · 2025Review
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3 authors.
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Abstract
We evaluated the performances of three BLUP and five Bayesian methods for genomic prediction by using nine actual and 54 simulated datasets. The genomic prediction accuracy was measured using Pearson's correlation coefficient between the genomic estimated breeding value (GEBV) and the observed phenotypic data using a fivefold cross-validation approach with 100 replications. The Bayesian alphabets performed better for the traits governed by a few genes/QTLs with relatively larger effects. On the contrary, the BLUP alphabets (GBLUP and CBLUP) exhibited higher genomic prediction accuracy for the traits controlled by several small-effect QTLs. Additionally, Bayesian methods performed better for the highly heritable traits and, for other traits, performed at par with the BLUP methods. Further, genomic BLUP (GBLUP) was identified as the least biased method for the GEBV estimation. Among the Bayesian methods, the Bayesian ridge regression and Bayesian LASSO were less biased than other Bayesian alphabets. Nonetheless, genomic prediction accuracy increased with an increase in trait heritability, irrespective of the sample size, marker density, and the QTL type (major/minor effect). In sum, this study provides valuable information regarding the choice of the selection method for genomic prediction in different breeding programs.
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